Toronto Dwellings Analysis¶

In this assignment, you will perform fundamental analysis for the Toronto dwellings market to allow potential real estate investors to choose rental investment properties.

In [1]:
# imports
import panel as pn
pn.extension('plotly')
import plotly.express as px
import pandas as pd
import hvplot.pandas
import matplotlib.pyplot as plt
import os
import random
from pathlib import Path
from dotenv import load_dotenv
In [2]:
# Read the Mapbox API key
load_dotenv()

map_box_api = os.getenv("map_box_api")
# Set the Mapbox API
px.set_mapbox_access_token(map_box_api)
In [3]:
type("mapbox")
Out[3]:
str

Load Data¶

In [4]:
# Read the census data into a Pandas DataFrame
file_path = Path("Data/toronto_neighbourhoods_census_data.csv")
to_data = pd.read_csv(file_path, index_col="year")
to_data.head()
Out[4]:
neighbourhood single_detached_house apartment_five_storeys_plus movable_dwelling semi_detached_house row_house duplex apartment_five_storeys_less other_house average_house_value shelter_costs_owned shelter_costs_rented
year
2001 Agincourt North 3715 1480 0 1055 1295 195 185 5 200388 810 870
2001 Agincourt South-Malvern West 3250 1835 0 545 455 105 425 0 203047 806 892
2001 Alderwood 3175 315 0 470 50 185 370 0 259998 817 924
2001 Annex 1060 6090 5 1980 605 275 3710 165 453850 1027 1378
2001 Banbury-Don Mills 3615 4465 0 240 380 15 1360 0 371864 1007 1163

Dwelling Types Per Year¶

In this section, you will calculate the number of dwelling types per year. Visualize the results using bar charts and the Pandas plot function.

Hint: Use the Pandas groupby function.

Optional challenge: Plot each bar chart in a different color.

In [5]:
# Calculate the sum number of dwelling types units per year (hint: use groupby)
dwelling_per_year = to_data.groupby("year").sum().drop(columns=["average_house_value", "shelter_costs_owned", "shelter_costs_rented" ])

dwelling_per_year
Out[5]:
single_detached_house apartment_five_storeys_plus movable_dwelling semi_detached_house row_house duplex apartment_five_storeys_less other_house
year
2001 300930 355015 75 90995 52355 23785 116900 3040
2006 266860 379400 165 69430 54690 44095 162850 1335
2011 274940 429220 100 72480 60355 44750 163895 2165
2016 269680 493270 95 71200 61565 48585 165575 2845
In [6]:
# Save the dataframe as a csv file
dwelling_per_year.to_csv("Data/dwelling_types_per_year.csv")
In [7]:
# Helper create_bar_chart function
def create_bar_chart(data, title, xlabel, ylabel, color):
    """
    Create a barplot based in the data argument.
    Input: 
    data = DataFrame to use for plotting the data
    title = Chart Title
    xlabel = Label for X Axis
    ylabel = Label for Y Axis
    color = Colour of the bar chart

    """
    data.plot(kind="bar", xlabel=xlabel, ylabel=ylabel, color=color, title=title)
    
# Show the plot after plotting it, to avoid overwrite the previously plotted bar chart.
    plt.show()
In [8]:
# Create a bar chart per year to show the number of dwelling types
colors = ["red", "blue","purple","green"]

#For loop is used to do charts for each year using a random color from the colors list.

for index in dwelling_per_year.index:
    title = "dwelling_types_in_Toronto in " + str(index)
    ylabel = "dwelling_type_units"
    create_bar_chart(dwelling_per_year.loc[index], title=title, xlabel=index, ylabel=ylabel, color = random.choice(colors))

Average Monthly Shelter Costs in Toronto Per Year¶

In this section, you will calculate the average monthly shelter costs for owned and rented dwellings and the average house value for each year. Plot the results as a line chart.

Optional challenge: Plot each line chart in a different color.

In [9]:
# Calculate the average monthly shelter costs for owned and rented dwellings
monthly_shelter_cost_per_year = to_data[["shelter_costs_owned","shelter_costs_rented"]].groupby("year").mean()
monthly_shelter_cost_per_year
Out[9]:
shelter_costs_owned shelter_costs_rented
year
2001 846.878571 1085.935714
2006 1316.800000 925.414286
2011 1448.214286 1019.792857
2016 1761.314286 1256.321429
In [10]:
# Helper create_line_chart function
def create_line_chart(data, title, xlabel, ylabel, color):
    """
    Create a line chart based in the data argument.
    Input: 
    data = DataFrame to use for plotting the data
    title = Chart Title
    xlabel = Label for X Axis
    ylabel = Label for Y Axis
    color = Colour of the line chart
    """
    data.plot(kind="line", xlabel=xlabel, ylabel=ylabel, color=color, title=title)
# Show the plot after plotting it, to avoid overwrite the previously plotted bar chart.
plt.show()
In [11]:
# Create two line charts, one to plot the monthly shelter costs for owned dwelleing and other for rented dwellings per year

# Line chart for owned dwellings
create_line_chart(data=monthly_shelter_cost_per_year["shelter_costs_owned"], title="Average Monthly Shelter Cost for Owned Dwellings in Toronto ", 
                  xlabel="Year", ylabel="Average Monthly Shelter Costs", color="orange")
In [12]:
# Line chart for rented dwellings
create_line_chart(data=monthly_shelter_cost_per_year["shelter_costs_rented"], title="Average Monthly Shelter Cost for Rented Dwellings in Toronto ", 
                  xlabel="Year", ylabel="Average Monthly Shelter Costs", color="yellow")

Average House Value per Year¶

In this section, you want to determine the average house value per year. An investor may want to understand better the sales price of the rental property over time. For example, a customer will want to know if they should expect an increase or decrease in the property value over time so they can determine how long to hold the rental property. You will visualize the average_house_value per year as a bar chart.

In [13]:
# Calculate the average house value per year
average_house_value = to_data["average_house_value"].groupby("year").mean()
average_house_value
Out[13]:
year
2001    289882.885714
2006    424059.664286
2011    530424.721429
2016    664068.328571
Name: average_house_value, dtype: float64
In [14]:
# Plot the average house value per year as a line chart
create_line_chart(data=average_house_value, title="Average House Value in Toronto ", 
                  xlabel="Year", ylabel="Average House Value", color="red")

Average House Value by Neighbourhood¶

In this section, you will use hvplot to create an interactive visualization of the average house value with a dropdown selector for the neighbourhood.

Hint: It will be easier to create a new DataFrame from grouping the data and calculating the mean house values for each year and neighbourhood.

In [15]:
# Create a new DataFrame with the mean house values by neighbourhood per year
avg_house_value_by_neighbourhood = to_data[["neighbourhood","average_house_value"]].reset_index()
avg_house_value_by_neighbourhood.head(10)
Out[15]:
year neighbourhood average_house_value
0 2001 Agincourt North 200388
1 2001 Agincourt South-Malvern West 203047
2 2001 Alderwood 259998
3 2001 Annex 453850
4 2001 Banbury-Don Mills 371864
5 2001 Bathurst Manor 304749
6 2001 Bay Street Corridor 257404
7 2001 Bayview Village 327644
8 2001 Bayview Woods-Steeles 343535
9 2001 Bedford Park-Nortown 565304
In [16]:
# Use hvplot to create an interactive line chart of the average house value per neighbourhood
# The plot should have a dropdown selector for the neighbourhood
avg_house_value_by_neighbourhood.hvplot.line(x="year", y="average_house_value", xlabel="Year", ylabel="Average House Value", groupby="neighbourhood")
Out[16]:

Number of Dwelling Types per Year¶

In this section, you will use hvplot to create an interactive visualization of the average number of dwelling types per year with a dropdown selector for the neighbourhood.

In [17]:
# Fetch the data of all dwelling types per year
dwelling_types_per_year = to_data.drop(columns=["average_house_value", "shelter_costs_owned", "shelter_costs_rented" ])
dwelling_types_per_year.head(10)
Out[17]:
neighbourhood single_detached_house apartment_five_storeys_plus movable_dwelling semi_detached_house row_house duplex apartment_five_storeys_less other_house
year
2001 Agincourt North 3715 1480 0 1055 1295 195 185 5
2001 Agincourt South-Malvern West 3250 1835 0 545 455 105 425 0
2001 Alderwood 3175 315 0 470 50 185 370 0
2001 Annex 1060 6090 5 1980 605 275 3710 165
2001 Banbury-Don Mills 3615 4465 0 240 380 15 1360 0
2001 Bathurst Manor 2405 1550 0 130 130 375 745 0
2001 Bay Street Corridor 10 7575 0 0 15 0 240 0
2001 Bayview Village 2170 630 0 170 765 15 640 0
2001 Bayview Woods-Steeles 1650 1715 0 925 105 10 170 5
2001 Bedford Park-Nortown 4985 2080 0 45 40 210 1235 15
In [18]:
# Use hvplot to create an interactive bar chart of the number of dwelling types per neighbourhood
# The plot should have a dropdown selector for the neighbourhood
dwelling_types_per_year.hvplot.bar(groupby="neighbourhood",rot=90, ylabel="Dwelling Type Units", xlabel="Year", height=500)
Out[18]:

The Top 10 Most Expensive Neighbourhoods¶

In this section, you will need to calculate the house value for each neighbourhood and then sort the values to obtain the top 10 most expensive neighbourhoods on average. Plot the results as a bar chart.

In [19]:
# Getting the data from the top 10 expensive neighbourhoods
ten_most_expensive_neighbourhoods = to_data.groupby("neighbourhood").mean().sort_values(by="average_house_value", ascending=False).reset_index()
ten_most_expensive_neighbourhoods.head(10)
Out[19]:
neighbourhood single_detached_house apartment_five_storeys_plus movable_dwelling semi_detached_house row_house duplex apartment_five_storeys_less other_house average_house_value shelter_costs_owned shelter_costs_rented
0 Bridle Path-Sunnybrook-York Mills 2260.00 331.25 0.00 36.25 90.00 25.0 40.00 0.00 1526485.75 2360.75 2321.75
1 Forest Hill South 1742.50 2031.25 1.25 61.25 45.00 75.0 1027.50 3.75 1195992.50 1781.00 1313.75
2 Lawrence Park South 3472.50 773.75 0.00 126.25 38.75 225.0 966.25 16.25 1094027.75 1954.00 1372.75
3 Rosedale-Moore Park 2498.75 4641.25 0.00 486.25 245.00 327.5 1618.75 2.50 1093640.00 1909.75 1537.25
4 St.Andrew-Windfields 3225.00 1670.00 0.00 185.00 552.50 97.5 586.25 5.00 999107.00 1880.25 1384.50
5 Casa Loma 916.25 2310.00 0.00 288.75 201.25 162.5 1192.50 2.50 981064.25 1873.75 1547.75
6 Bedford Park-Nortown 4865.00 1981.25 0.00 43.75 57.50 287.5 1275.00 88.75 930415.25 1786.75 1255.00
7 Forest Hill North 1488.75 3392.50 0.00 12.50 16.25 82.5 402.50 1.25 851680.50 1722.75 1245.50
8 Kingsway South 2326.25 576.25 0.00 66.25 48.75 20.0 336.25 2.50 843234.25 1736.75 1622.00
9 Yonge-St.Clair 565.00 3948.75 0.00 425.00 212.50 172.5 1308.75 6.25 813220.25 1680.75 1369.00
In [20]:
# Plotting the data from the top 10 expensive neighbourhoods
ten_most_expensive_neighbourhoods.head(10).hvplot.bar(rot=90, ylabel="Average House Value", xlabel="Neighbourhood", y="average_house_value", x="neighbourhood",height=500)
Out[20]:

Neighbourhood Map¶

In this section, you will read in neighbourhoods location data and build an interactive map with the average house value per neighbourhood. Use a scatter_mapbox from Plotly express to create the visualization. Remember, you will need your Mapbox API key for this.

Load Location Data¶

In [21]:
# Load neighbourhoods coordinates data
file_path = Path("Data/toronto_neighbourhoods_coordinates.csv")
df_neighbourhood_locations = pd.read_csv(file_path)
df_neighbourhood_locations.head()
Out[21]:
neighbourhood lat lon
0 Agincourt North 43.805441 -79.266712
1 Agincourt South-Malvern West 43.788658 -79.265612
2 Alderwood 43.604937 -79.541611
3 Annex 43.671585 -79.404001
4 Banbury-Don Mills 43.737657 -79.349718

Data Preparation¶

You will need to join the location data with the mean values per neighbourhood.

  1. Calculate the mean values for each neighbourhood.

  2. Join the average values with the neighbourhood locations.

In [22]:
# Calculate the mean values for each neighborhood
mean_data_neighbourhoods = to_data.groupby("neighbourhood").mean().reset_index()
mean_data_neighbourhoods.head()
Out[22]:
neighbourhood single_detached_house apartment_five_storeys_plus movable_dwelling semi_detached_house row_house duplex apartment_five_storeys_less other_house average_house_value shelter_costs_owned shelter_costs_rented
0 Agincourt North 3435.00 1947.50 2.50 863.75 1406.25 512.50 547.50 10.00 329811.5 1109.00 983.50
1 Agincourt South-Malvern West 2897.50 2180.00 1.25 375.00 456.25 523.75 628.75 32.50 334189.0 1131.25 985.00
2 Alderwood 2903.75 302.50 1.25 503.75 76.25 302.50 502.50 1.25 427922.5 1166.75 1003.25
3 Annex 751.25 7235.00 1.25 1375.00 613.75 355.00 4605.00 83.75 746977.0 1692.75 1315.25
4 Banbury-Don Mills 3572.50 5388.75 1.25 273.75 626.25 32.50 1340.00 0.00 612039.0 1463.50 1242.75
In [23]:
# Join the average values with the neighbourhood locations
mean_data_neighbourhoods.set_index(keys="neighbourhood",inplace=True)
df_neighbourhood_locations.set_index(keys="neighbourhood",inplace=True)
neighbourhood_with_location = pd.concat([mean_data_neighbourhoods, df_neighbourhood_locations ], join="inner", axis="columns")
neighbourhood_with_location
Out[23]:
single_detached_house apartment_five_storeys_plus movable_dwelling semi_detached_house row_house duplex apartment_five_storeys_less other_house average_house_value shelter_costs_owned shelter_costs_rented lat lon
neighbourhood
Agincourt North 3435.00 1947.50 2.50 863.75 1406.25 512.50 547.50 10.00 329811.50 1109.00 983.50 43.805441 -79.266712
Agincourt South-Malvern West 2897.50 2180.00 1.25 375.00 456.25 523.75 628.75 32.50 334189.00 1131.25 985.00 43.788658 -79.265612
Alderwood 2903.75 302.50 1.25 503.75 76.25 302.50 502.50 1.25 427922.50 1166.75 1003.25 43.604937 -79.541611
Annex 751.25 7235.00 1.25 1375.00 613.75 355.00 4605.00 83.75 746977.00 1692.75 1315.25 43.671585 -79.404001
Banbury-Don Mills 3572.50 5388.75 1.25 273.75 626.25 32.50 1340.00 0.00 612039.00 1463.50 1242.75 43.737657 -79.349718
... ... ... ... ... ... ... ... ... ... ... ... ... ...
Wychwood 1056.25 1236.25 0.00 992.50 298.75 325.00 1878.75 17.50 565976.50 1390.75 1017.25 43.676919 -79.425515
Yonge-Eglinton 1468.75 1638.75 0.00 470.00 33.75 328.75 1385.00 6.25 809745.75 1799.50 1347.75 43.704689 -79.403590
Yonge-St.Clair 565.00 3948.75 0.00 425.00 212.50 172.50 1308.75 6.25 813220.25 1680.75 1369.00 43.687859 -79.397871
York University Heights 1355.00 5165.00 1.25 1316.25 662.50 188.75 1085.00 33.75 305899.50 1116.75 937.50 43.765736 -79.488883
Yorkdale-Glen Park 2286.25 1347.50 0.00 73.75 450.00 377.50 722.50 7.50 430861.25 1122.50 942.50 43.714672 -79.457108

140 rows × 13 columns

In [ ]:

Mapbox Visualization¶

Plot the average values per neighbourhood using a Plotly express scatter_mapbox visualization.

In [24]:
# Create a scatter mapbox to analyze neighbourhood info
average_house_value_map= px.scatter_mapbox(

            neighbourhood_with_location,
            lat="lat",
            lon="lon",
            color="average_house_value", 
            color_continuous_scale=px.colors.cyclical.Twilight,
            title="Average House Value in Toronto",
            zoom=9,
            width=700,
            height=600,

        
   
    )

 

average_house_value_map.show()

Cost Analysis - Optional Challenge¶

In this section, you will use Plotly express to a couple of plots that investors can interactively filter and explore various factors related to the house value of the Toronto's neighbourhoods.

Create a bar chart row facet to plot the average house values for all Toronto's neighbourhoods per year¶

In [25]:
mean_data_neighbourhoods.reset_index(inplace=True)
to_data
Out[25]:
neighbourhood single_detached_house apartment_five_storeys_plus movable_dwelling semi_detached_house row_house duplex apartment_five_storeys_less other_house average_house_value shelter_costs_owned shelter_costs_rented
year
2001 Agincourt North 3715 1480 0 1055 1295 195 185 5 200388 810 870
2001 Agincourt South-Malvern West 3250 1835 0 545 455 105 425 0 203047 806 892
2001 Alderwood 3175 315 0 470 50 185 370 0 259998 817 924
2001 Annex 1060 6090 5 1980 605 275 3710 165 453850 1027 1378
2001 Banbury-Don Mills 3615 4465 0 240 380 15 1360 0 371864 1007 1163
... ... ... ... ... ... ... ... ... ... ... ... ...
2016 Wychwood 920 1295 0 880 290 395 2080 35 787760 1864 1146
2016 Yonge-Eglinton 1400 1995 0 465 60 310 1445 0 1127052 2398 1535
2016 Yonge-St.Clair 520 4315 0 450 220 130 1370 0 1131888 2192 1619
2016 York University Heights 1235 5505 0 1360 775 280 995 0 425769 1444 1122
2016 Yorkdale-Glen Park 2165 1185 0 80 600 465 830 5 599698 1451 1128

560 rows × 12 columns

In [26]:
px.bar(to_data, x="neighbourhood", y="average_house_value",color="average_house_value", facet_row=to_data.index,height=1000, title="Average House Values in Toronto Per Neighbourhood")

Create a sunburst chart to conduct a costs analysis of most expensive neighbourhoods in Toronto per year¶

In [27]:
# Fetch the data from all expensive neighbourhoods per year.
ten_most_expensive_neighbourhoods
Out[27]:
neighbourhood single_detached_house apartment_five_storeys_plus movable_dwelling semi_detached_house row_house duplex apartment_five_storeys_less other_house average_house_value shelter_costs_owned shelter_costs_rented
0 Bridle Path-Sunnybrook-York Mills 2260.00 331.25 0.00 36.25 90.00 25.00 40.00 0.00 1526485.75 2360.75 2321.75
1 Forest Hill South 1742.50 2031.25 1.25 61.25 45.00 75.00 1027.50 3.75 1195992.50 1781.00 1313.75
2 Lawrence Park South 3472.50 773.75 0.00 126.25 38.75 225.00 966.25 16.25 1094027.75 1954.00 1372.75
3 Rosedale-Moore Park 2498.75 4641.25 0.00 486.25 245.00 327.50 1618.75 2.50 1093640.00 1909.75 1537.25
4 St.Andrew-Windfields 3225.00 1670.00 0.00 185.00 552.50 97.50 586.25 5.00 999107.00 1880.25 1384.50
... ... ... ... ... ... ... ... ... ... ... ... ...
135 Malvern 3693.75 2982.50 0.00 1317.50 3410.00 878.75 552.50 55.00 256880.25 1228.00 998.75
136 Thorncliffe Park 3.75 6318.75 0.00 0.00 141.25 0.00 187.50 5.00 255264.75 1304.25 1088.50
137 Mount Olive-Silverstone-Jamestown 1730.00 5916.25 1.25 176.25 1033.75 603.75 127.50 1.25 253182.25 1159.50 955.25
138 Taylor-Massey 622.50 4795.00 0.00 203.75 216.25 150.00 218.75 15.00 239414.00 1129.25 919.25
139 Flemingdon Park 5.00 6368.75 0.00 182.50 630.00 3.75 591.25 0.00 207799.00 1199.75 973.00

140 rows × 12 columns

In [28]:
# Create the sunburst chart
px.sunburst(
    ten_most_expensive_neighbourhoods,
    path=[ten_most_expensive_neighbourhoods.index, 'neighbourhood'], values='average_house_value', color='average_house_value', color_continuous_scale='blues',
    height=500
    
)
In [ ]: